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This plot shows the pipeline performance overview. Some metrics are calculated.

• Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants
• Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25.
• Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544 ).
• Heatmap score [MC]: the fraction (0% - 100%) of fully cleaved peptides per Raw file
• Heatmap score [MC Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study.
• Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad.
• Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides.
• Heatmap score [Pep Missing]: Linear scale of the fraction of missing peptides.

This table shows the quantms pipeline summary statistics.

Including Sample Name、Possible Study Variables、identified the number of peptide in the pipeline、 and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the remove_decoy parameter.

This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.

This table shows the changes in the number of spectra corresponding to each input file during the pipeline operation. And the number of peptides finally identified and quantified is obtained from the PSM table in the mzTab file. You can also remove decoys with the remove_decoy parameter.:

• MS1_Num: The number of MS1 spectra extracted from mzMLs
• MS2_Num: The number of MS2 spectra extracted from mzMLs
• MSGF: The Number of spectra identified by MSGF search engine
• Comet: The Number of spectra identified by Comet search engine
• Sage: The Number of spectra identified by Sage search engine
• PSMs from quant. peptides: extracted from PSM table in mzTab file
• Peptides quantified: extracted from PSM table in mzTab file

This statistic is extracted from idXML files. SpecEvalue: Spectral E-values, the search score of MSGF. The value used for plotting is -lg(SpecEvalue).

This statistic is extracted from idXML files. xcorr: cross-correlation scores, the search score of Comet. The value used for plotting is xcorr.

This statistic is extracted from idXML files.

Consensus support is a measure of agreement between search engines. Every peptide sequence in the analysis has been identified by at least one search run. The consensus support defines which fraction (between 0 and 1) of the remaining search runs "supported" a peptide identification that was kept. The meaning of "support" differs slightly between algorithms: For best, worst, average and rank, each search run supports peptides that it has also identified among its top considered_hits candidates. So the "consensus support" simply gives the fraction of additional search engines that have identified a peptide. (For example, if there are three search runs, peptides identified by two of them will have a "support" of 0.5.) For the similarity-based algorithms PEPMatrix and PEPIons, the "support" for a peptide is the average similarity of the most-similar peptide from each (other) search run.

This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.

This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).

Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.

For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.

• Heatmap score [EVD: MS2 Oversampling]: The percentage of non-oversampled 3D-peaks.

Mass deltas close to zero reflect more accurate identifications and also that the reporting of the amino acid modifications and charges have been done accurately. This plot can highlight systematic bias if not centered on zero. Other distributions can reflect modifications not being reported properly. Also it is easy to see the different between the target and the decoys identifications.

The quantification information of peptides is obtained from the MSstats input file. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms.

• BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to 1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab peptide table for DDA datasets.
• Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored.
• Peptide intensity in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Summarize intensity of fractions, and then mean intensity in technical replicates/biological replicates separately.

The quantification information of proteins is obtained from the msstats input file. The table shows the quantitative level and distribution of proteins in different study variables and run.

• Peptides_Number: The number of peptides for each protein.
• Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored.
• Protein intensity in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Summarize intensity of peptides.